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Abstract Understanding crop plants responses to abiotic stress is increasingly important in this changing climate. We asked experts how discoveries in Arabidopsis thaliana have translated into advancements in abiotic crop stress resilience. The theme is that core regulatory networks identified in Arabidopsis are conserved in crops, but the molecular regulation varies among species. For cold tolerance, the regulatory framework is conserved, but MAP Kinase signaling promotes degradation of the INDUCER OF DREB1 EXPRESSION transcription factor in Arabidopsis but inhibits it in rice. For hypoxia, manipulation of the oxygen sensing Arg/N-degron pathway discovered in Arabidopsis has improved waterlogging and flood tolerance in barley, maize, wheat, and soybean. For light signaling, overexpression of PHYTOCHROME B reduces shade avoidance, improving yield under dense planting in potato, soybean, and maize. In rice, understanding of nitrogen responsiveness, uptake, and transport in Arabidopsis has inspired engineering of the NRT1 nitrate transceptor to increase yield. Arabidopsis research has provided leads for genetic manipulations that may improve drought resilience in crop species. Growing plants in space generates a complex array of stresses, and Arabidopsis experiments in the space station prepare for future development of robust crops as integral components of the life support systems. For environmental regulation of flowering time, the role of the GIGANTEA - CONTANS - FLOWERING LOCUS T module elucidated in Arabidopsis is largely conserved in crop plants, although additional regulators modify short-day responsiveness in rice, soybean, chrysanthemum, and potato. 

The rapid expansion of social media platforms has provided unprecedented access to massive amounts of multimodal user-generated content. Comprehending user emotions can provide valuable insights for improving communication and understanding of human behaviors. Despite significant advancements in Affective Computing, the diverse factors influencing user emotions in social networks remain relatively understudied. Moreover, there is a notable lack of deep learning-based methods for predicting user emotions in social networks, which could be addressed by leveraging the extensive multimodal data available. This work presents a novel formulation of personalized emotion prediction in social networks based on heterogeneous graph learning. Building upon this formulation, we design HMG-Emo, a Heterogeneous Multimodal Graph Learning Framework that utilizes deep learning-based features for user emotion recognition. Additionally, we include a dynamic context fusion module in HMG-Emo that is capable of adaptively integrating the different modalities in social media data. Through extensive experiments, we demonstrate the effectiveness of HMG-Emo and verify the superiority of adopting a graph neural network-based approach, which outperforms existing baselines that use rich hand-crafted features. To the best of our knowledge, HMG-Emo is the first multimodal and deep-learning-based approach to predict personalized emotions within online social networks. Our work highlights the significance of exploiting advanced deep learning techniques for less-explored problems in Affective Computing. 

Strange stars ought to exist in the universe according to the strange quark matter hypothesis, which states that matter made of roughly equal numbers of up, down, and strange quarks could be the true ground state of baryonic matter rather than ordinary atomic nuclei. Theoretical models of strange quark matter, such as the standard MIT bag model, the density-dependent quark mass model, or the quasi-particle model, however, appear to be unable to reproduce some of the properties (masses, radii, and tidal deformabilities) of recently observed compact stars. This is different if alternative gravity theory (e.g., non-Newtonian gravity) or dark matter (e.g., mirror dark matter) are considered, which resolve these issues. The possible existence of strange stars could thus provide a clue to new physics, as discussed in this review.